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Delivery Lead

Job in Slough, Berkshire, SL1, England, UK
Listing for: i3
Part Time position
Listed on 2026-09-21
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 90000 - 120000 GBP Yearly GBP 90000.00 120000.00 YEAR
Job Description & How to Apply Below

Data Science & AI Delivery Lead

London

Hybrid – minimum 3 days per week in the local office

£90,000–£120,000 base + 15–20% target bonus

We are looking for an experienced Data Science & AI Delivery Lead to join a major international organisation investing significantly in Data, AI and advanced analytics.

This is a senior, hands-on technical leadership position for someone who can combine AI/ML engineering expertise with delivery leadership
. You’ll act as the deputy to the Head of Data Science & AI Delivery, taking responsibility for the day-to-day execution of AI initiatives and helping a growing team take solutions from concept and proof of value through to scalable production deployments.

The organisation sees Data and AI as a major strategic capability – using information to make better decisions, identify opportunities earlier, improve operational efficiency and create competitive advantage.

Importantly,
this is not a purely managerial role
. You’ll remain close to the technology, working directly with the codebase, reviewing technical designs and architecture, establishing engineering standards and contributing hands-on when required.

You’ll take a leading role in the technical execution and delivery of AI, machine learning and advanced analytics solutions, including:

  • Owning the day-to-day running of AI delivery work streams, ensuring teams remain focused, unblocked and aligned to priorities.
  • Acting as deputy to the Head of Data Science & AI Delivery and providing leadership continuity across projects and stakeholder forums.
  • Taking technical delivery accountability from initial concept through development, deployment and production.
  • Leading AI solution architecture
    , technical design reviews, implementation approaches and production-readiness assessments.
  • Establishing engineering standards, reusable frameworks, patterns and best practices for AI delivery.
  • Providing technical guidance around solution design, model selection and architecture.
  • Remaining hands-on with Python development
    , particularly during critical delivery phases and proof-of-concept work.
  • Reviewing code and technical outputs to maintain high engineering and quality standards.
  • Designing and delivering Generative AI and LLM solutions
    , including RAG architectures, prompt engineering, vector search and integration with Azure AI services.
  • Defining engineering approaches for LLM applications, agentic AI systems, machine learning solutions and AI platforms
    .
  • Establishing strong MLOps practices covering model versioning, automated testing, deployment pipelines, monitoring, observability and model lifecycle management.
  • Ensuring solutions meet appropriate security, governance, scalability, explainability and operational support requirements.
  • Evaluating emerging AI technologies and determining where they can deliver meaningful business value.
  • Working closely with architecture, technology, programme and Data Engineering teams to ensure effective end-to-end delivery.
  • Supporting delivery planning, timelines, cost estimates, cloud/API consumption and resource allocation.
  • Mentoring Data Scientists and AI Engineers through architecture reviews, code reviews, pair programming and technical coaching.
  • Helping create a high-performing engineering culture focused on collaboration, quality and continuous learning.

You’ll need demonstrable experience delivering AI and machine learning solutions into production within a professional environment, alongside the technical credibility to lead experienced Data Scientists and AI Engineers.

Key experience includes:

  • Expert-level Python development.
  • Strong experience with core data science and machine learning libraries such as scikit-learn, pandas, PyTorch and/or Tensor Flow
    .
  • Practical experience with Databricks
    , including…
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